Channel Estimation Based on IOTA Filter in OFDM/OQPSK and OFDM/OQAM Systems

Zhou, Xiao; Wang, Chengyou; Tang, Ruiguang

doi:10.3390/app9071454

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…Therefore, choosing the right power allocation factor is the key. In order to optimize the error performance of the system after equalization, this paper uses the output SNR maximum criterion to obtain the optimal power allocation factor [26]. The assumptions in the analysis process are as follows:…”

Section: Power Allocation Problem In Implicit Sequence Channel Estimamentioning

confidence: 99%

“…In Equation (26), definedc(n) = σ −1 c c(n). Assuming that this paper uses an m-sequence with good autocorrelation properties, Equation (26) can be simplified as:…”

Section: Power Allocation Problem In Implicit Sequence Channel Estimamentioning

confidence: 99%

“…It can be seen from Equation (27) that the power of the summed noise is related to the average power σ 2 n input noise before equalization and the channel estimation MSE σ 2 e . Wherein, the MSE σ 2 e has been obtained in the foregoing deviation, and the power of the input pre-equalization noise can be obtained by a method based on threshold decision and sequence separation [17], [23], [26].…”

Section: Power Allocation Problem In Implicit Sequence Channel Estimamentioning

confidence: 99%

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An Efficient Method for Offset Mitigation in Free-Space Optical Systems

2019

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Channel estimation is a key feature for free space optical (FSO) wireless communication systems which are required for a high QoS and high data rate system. Aiming at the problem of FSO wireless channel estimation, this paper proposes an implicit sequence channel estimation scheme to eliminate dc offset, and studies the key factors affecting the system performance. The optimal power allocation factor is derived based on the maximum SNR criterion. The system performance parameters such as the bit error rate (BER), mean square error (MSE), and computational complexity are evaluated. The results show that under the same dc bias condition, the MSE is estimated to decrease significantly after using the method of this paper. With the increase of dc offset d, the system error rate is significantly reduced and more approaching d = 0 (i.e., no dc offset). The proposed algorithm effectively mitigates the dc offset impact on the system and the BER is significantly improved while the MSE is reduced to 50% of the traditional state-of-the-art method. The proposed work can provide a technical solution to the FSO system and give support to technological development.

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Section: Power Allocation Problem In Implicit Sequence Channel Estimamentioning

confidence: 99%

“…In Equation (26), definedc(n) = σ −1 c c(n). Assuming that this paper uses an m-sequence with good autocorrelation properties, Equation (26) can be simplified as:…”

Section: Power Allocation Problem In Implicit Sequence Channel Estimamentioning

confidence: 99%

Section: Power Allocation Problem In Implicit Sequence Channel Estimamentioning

confidence: 99%

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An Efficient Method for Offset Mitigation in Free-Space Optical Systems

2019

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“…Underwater acoustic MPSK is widely used in information transmission; it is mainly used in high-speed communication scenarios [1,2]. MPSK can also be used as a part of Direct Sequence Spread Spectrum (DSSS) to achieve long range data transmission [3,4], and has been abundantly used for underwater Orthogonal Frequency Division Multiplexing (OFDM) subcarrier modulation [5][6][7]. With the development of underwater acoustic communication technology and the increasing demand in the field of underwater acoustic signal detection, modulation identification of non-cooperative MPSK has become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization

et al. 2020

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This paper presents a likelihood-based algorithm for identifying different phase shift keying (PSK) modulations, i.e., BPSK, QPSK, and 8PSK. This algorithm selects the modulation type that maximizes a loglikelihood function that is based on the known original constellation associated with the constellation of the received signals for the candidate modulation types. However, there are two problems in non-cooperative underwater acoustic Multiple Phase Shift Keying (MPSK) modulation identification based on the likelihood method. One is the original constellation, which as prior information is unknown. The other is the underwater acoustic multipath channel makes the constellation distort seriously. In this paper, we solved these problems by combining sparse bayesian learning (SBL) with expectation maximization (EM). The specific steps are as follows. Firstly, blind channel equalization can be achieved by channel impulse response (CIR), which is estimated by sparse bayesian learning in single input multi output (SIMO) underwater acoustic channel. Subsequently, we used expectation maximization to compensate amplitude attenuation and phase offset, as the original constellation of MPSK is unknown. Finally, modulation can be successfully identified by the Quasi Hybrid Likelihood Ratio Test (QHLRT). The simulation results show that the channel estimation method based on SBL can eliminate the influence of channel effectively, and the EM algorithm can make the received constellation converge to the preset constellation in the case of unknown original transmit constellation, which effectively solves these two problems. We use the proposed SBL-EM-QHLRT method to achieve an identification rate of more than 95% in underwater acoustic multipath channels with Signal to Noise Ratio (SNR) higher than 15 dB, which provides a new idea for modulation identification of non-cooperative underwater acoustic MPSK.

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“…The theoretical analysis has been validated by extensive Montel Carlo simulation results of the O-OFDM/OQAM systems by using commercial software VPI Transmissionmaker 10.1. As numerous simulation results show, both BER performance and launch power of the O-OFDM/OQAM system could be improved evidently by adopting VENE based on the employment of three typical filters, namely the Gaussian filter [46], the extended Gaussian filter [47], and the isotropic orthogonal transform algorithm (IOTA) filter [48]. The combination of VENE with the IOTA filter demonstrates optimal transmission performance due to the promising TFL property of IOTA filter.…”

Section: Introductionmentioning

confidence: 99%

Volterra Expansion Based Intra Channel Nonlinear Effect Equalization Method for Optical OFDM/OQAM Systems

Fang

et al. 2020

IEEE Access

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Channel Estimation Based on IOTA Filter in OFDM/OQPSK and OFDM/OQAM Systems

Cited by 7 publications

References 21 publications

An Efficient Method for Offset Mitigation in Free-Space Optical Systems

An Efficient Method for Offset Mitigation in Free-Space Optical Systems

Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization

Volterra Expansion Based Intra Channel Nonlinear Effect Equalization Method for Optical OFDM/OQAM Systems

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